Dear Deepak,

There is no such thing as a "best" interpolation technique, in absolute terms. What you need to ask is "What is the most appropriate interpolation technique", and that, in turn, will depend on the difference in spatial resolution between the gridded data set you are starting from and the spatial resolution of the product you want to generate, the expected accuracy of the final product, and possibly further requirements such as the need to preserve spatial features, for instance. Another possible constraint might be the availability of a computer program or procedure to implement the selected algorithm.

If you are looking for a simple solution, linear interpolation may be sufficient, and it is easy to implement (http://en.wikipedia.org/wiki/Linear_interpolation). Cubic splines may be better for non-linear interpolation (http://en.wikipedia.org/wiki/Spline_interpolation), though you need to be clear about the need to select a more complex algorithm.

If you have access to mathematical or statistical packages such as Wolfram's Mathematica (http://reference.wolfram.com/language/ref/Interpolation.html), or Exelis' IDL (http://www.exelisvis.com/docs/INTERPOLATE.html), or the R language (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/approxfun.html), for instance, you'll find a number of interpolation schemes already implemented and ready to use. The main question is not so much to pick one approach but to select the one that best matches your actual needs as well as your available resources.

Good luck, Michel.

Thanks Sir for answering the query.

You can see the following site:

Exelis' IDL (http://www.exelisvis.com/docs/INTERPOLATE.html)